It is of the greatest importance to improve early screening and detection of risk for autism, a genetically complex neural developmental disorder affecting higher order functions such as social, communication, language and cognition. Among the benefits of early detection is that accelerating the pace of identification and treatment by even a year1 can have a considerable impact on the outcome of affected newborns, infants, toddlers and young children.
Despite recent university-based research advances in the development of potential methods for screening, detection and diagnostic evaluation for autism within the first 2 years of life, the clinical translation of these methods into widespread and effective community practice in the US has not occurred. Instead, 3 to 5 years of age continues to be the age of first clinical identification and referral for treatment services for autism in much of the US1. Studies find that on average, a child with autism is diagnostically evaluated by 4 to 5 different professionals before a final diagnosis is determined and this process can take several years during which the child does not receive suitable treatment. From a neurobiological perspective, this is particularly problematic given that functional connections in the brain are strongly established during the first few years of life2, 3. Starting treatment after many neural connections have already been formed (rather than before) will likely reduce treatment efficacy and impact. Hundreds of websites, articles, blogs and government, professional and private organizations cite the need for the early screening, detection, diagnosis and treatment referral for children with autism, yet the gulf separating university-based research advances in early detection and actual community clinical practice is alarming; For example, in 2012 the CDC documented the median age of autism identification in the US (based on 2008 data) is about 4 years1. The median age of treatment referral is correspondingly even later in the US. Further, there remain large underserved segments of the population, both in terms of early screening and access to empirically-validated early intervention. The magnitude of the problem is staggering: Given recent prevalence estimates and the U.S. birth rate, every year 52,000 to 84,000 infants will go on to develop autism. Thus, there is an enormous and urgent need for useful and cost-effective pediatric population screening strategies in ordinary community settings throughout the U.S. Presently, unfortunately, hundreds of thousands of toddlers and young children with autism in the U.S. are overlooked, under-treated and may have a poorer outcome than need be.
Moreover, once children are identified with having an autism spectrum disorder (ASD), science has not yet offered insight into prognosis. Will the child face consistent extreme barriers in speech, language and social development, or will he or she fall into the minority of ASD individuals that enjoy success in school and beyond. Presently, however, there are no prognostic biomarkers of autism; specifically there is a lack of prognostic biomarkers that predict and characterize likely clinical, neural and treatment progress and outcome.
Despite the importance, the high priority of discovery of risk behavioral or biological markers with clinical impact remains largely unfulfilled. Neither biological nor behavioral markers have emerged that fulfill this need in clinical settings for the general pediatric population. For example, commonly used parent report screens (e.g., Modified Checklist for Autism in Toddlers (M-CHAT), Communication and Symbolic Behavior Scales (CSBS) have valuable strengths, but also weaknesses4-6, including very high false positive rates. The M-CHAT has very low specificity (27%5) and positive predictive value (PPV, 11%) when used in the general population7, rendering it of limited utility in routine clinical practice. Similarly, the newest and largest study to test the efficacy of the M-CHAT conducted by Chlebowski, Robins, Barton & Fein published in 2013 found an 80% false positive rate when the tool was used alone8. Although high-risk baby sib studies by Zwaigenbaum9, Ozonoff10, Paul11, Landa12 and others have revealed key early deficits such as abnormalities in social attention9, they report data only at the group level and have not reported validation statistics such as PPV that are a necessary first step for determining the utility of a behavioral trait as an early marker.
Several groups have used eye tracking and reported reduced preference for biological motion23, fixation to the eye region24, head region25 and difficulties in joint attention26 as well as scene monitoring during explicit dyadic cues27 in ASD relative to typically developing (TD) toddlers. While collectively these studies point to early developmental origins of social dysfunction, reported effects are subtle and results are provided only at the overall group level and have very weak power to detect or diagnose ASD. For example, in one study differences in fixation towards the face and eye region were no different between ASD and TD toddlers when toddlers watched a woman make a sandwich and only became evident during a specific 3-second dyadic bid condition27. Moreover, validation statistics that are needed to translate eye tracking into a screening tool, such as specificity or positive predictive value, are not provided in most eye tracking studies of ASD toddlers.
While great strides have been made in understanding possible genetic risk factors13-15 and neural bases16-18 of autism, neither gene nor brain abnormalities published to date have translated into practical clinical population screens or tests of risk for autism in toddlers. Also, links between genetic and neural developmental abnormalities at young ages have remained largely unknown. Overall, research on potential genetic and neuroimaging biomarkers has remained largely “in the lab.”
Discovery by one of the present inventors19 that a substantial percentage of autism infants and toddlers display early brain overgrowth indicates that autism might involve abnormalities in mechanisms that regulate cell production or natural apoptosis in early life. The inventor analyzed dysregulation of genetic mechanism in autism in two ways. First, the total number of neurons in prefrontal cortex tissue in postmortem autistic boys was counted to reveal a huge 67% excess of neurons18. Second, evidence shows that dysregulation of genetic mechanism that govern neuron number in prefrontal cortex brain tissue in postmortem autistic boys14.
These discoveries have advanced the general understanding of the neural and genetic bases of ASD but not the early screening of ASD risk, diagnostic evaluation, and prognostic assessment of autism at the level of the individual child in the general pediatric population. While other studies raise the hope that MRI neuro-imaging biomarkers might be identified for use with older children or adults already known to have autism, they have not demonstrated the ability to improve risk assessment at very young ages in the general pediatric population when they are most needed. Still other studies suffer from limitations such as being based only on data from multiplex ASD families18,19 leaving unaddressed the majority of autistic infants in the general population, or based on algorithms that identify genes with little or no demonstrated relevance to the underlying brain maldevelopment in autism20,21.
Broadly speaking, “biomarkers” to date (e.g., genetic, molecular, imaging) have poor diagnostic accuracy, specificity and/or sensitivity; none have clinical outcome prognostic power; most are expensive; none are suitable as an early screening tool in community populations; and few have undergone serious clinical scrutiny and rigorous validation. For example, genetic findings have been generally non-specific, and the best characterized CNVs can occur in schizophrenia, bipolar, intellectual disability as well as ASD (e.g., 16p11.2). Few gene mutations are recurrent22. CNVs and recurrent genes combined account for a very small, arguably about 5-10%, of all ASD individuals. Thus, current DNA tests detect only rare autism cases and lack specificity. Moreover, genetic tests released by several companies detect only a small percent (5% to 20%) of ASD individuals, generally lack good specificity (because CNV, gene mutation and SNP markers in these tests are also found in a wide variety of non-ASD disorders such as schizophrenia or bipolar as well as in non-symptomatic, “typical” individuals), miss the vast majority of ASD individuals and are very expensive and out of the reach of most individuals. A genetic test targeting baby sibs of older ASD children provides only estimates of risk from less to more, but of course, parents who already have a child with ASD already know subsequent offspring are at risk. The benefit from this test is arguably small and of little practical clinical utility. No genetic finding has been shown to have clinical outcome prognostic power; that is, genetic testing does not provide information about likely later language, social or general functional progress and ability. A recent MRI “biomarker” works on adults with ASD, but diagnosis of ASD in adults is of very limited clinical value. A diffusion tensor imaging (DTI) study of small samples of infant siblings of older ASD children shows group differences too small to hold diagnostic promise. A gene expression classifier of previously diagnosed ASD 5 to 11 year olds performed in a validation set with accuracy, sensitivity and specificity at only 67.7%, 69.2% and 65.9%, respectively21. A metabolomics classifier tested only a sample of 4 to 6.9 year old children previously diagnosed as ASD and did not test newborns or 0 up to 4 year olds.32 
In sum, no currently reported biomarker holds promise as a primary or secondary early developmental screen or an early diagnostic or prognostic tool in ordinary community pediatric settings at young ages from birth through early childhood when these clinical tools are most needed. There are no preclinical screens or tests for risk of developing ASD with the sensitivity and specificity for routine value in clinical application. Current expectations are that ASD is so etiologically and clinically heterogeneous that no diagnostic biomarker and/or combination of behavioral or biological markers is likely to do better that detect a small percentage of cases, and that such biomarkers and/or combination of behavioral and biological markers will be either sensitive but non-specific or specific but for a tiny portion of the ASD spectrum.